OpenCV图像处理技术（Python）——视频处理

    # 打开摄像头并灰度化显示
    import cv2
    cap = cv2.VideoCapture(0)
    while(cap.isOpened()):
    # 获取一帧
    ret, frame = cap.read()
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break
    cap.release()
    cv2.destroyAllWindows()

    ##思考问题1：如何将视频转换为灰度、如何将摄像头显示正常而不是镜像
    import cv2
    cap = cv2.VideoCapture(0)
    while(cap.isOpened()):
    # 获取一帧
    ret, frame = cap.read()
    ##图像处理
    frame1=cv2.flip(frame,1)
    gray=cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
    canny=cv2.Canny(gray,80,150)
    
    ##
    cv2.imshow('frame', canny)
    if cv2.waitKey(1) == ord('q'):
        break
    cap.release()
    cv2.destroyAllWindows()

    import cv2
    cap = cv2.VideoCapture(0)
    
    while(cap.isOpened()):
    # 获取一帧
    ret, frame = cap.read()
    ##图像处理
    frame1=cv2.flip(frame,1)
    gray=cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
    canny=cv2.Canny(gray,80,150)
    
    ##
    cv2.imshow('frame', canny)
    if cv2.waitKey(1) == ord('q'):
        break
    cap.release()
    cv2.destroyAllWindows()

    def meiyan(img):
    rows, cols = img.shape[:2]
    dst = np.zeros((rows, cols, 3), dtype="uint8")
    
    #图像怀旧特效
    for i in range(rows):
        for j in range(cols):
            B = 0.272*img[i,j][2] + 0.534*img[i,j][1] + 0.131*img[i,j][0]
            G = 0.349*img[i,j][2] + 0.686*img[i,j][1] + 0.168*img[i,j][0]
            R = 0.393*img[i,j][2] + 0.769*img[i,j][1] + 0.189*img[i,j][0]
            if B>255:
                B = 255
            if G>255:
                G = 255
            if R>255:
                R = 255
            dst[i,j] = np.uint8((B, G, R))
    return dst

    # 获得视频属性
    import cv2
    import numpy as np
    cap = cv2.VideoCapture(0)
    width, height = cap.get(3), cap.get(4)
    while(cap.isOpened()):
    ret, frame = cap.read()
    frame = meiyan(frame)
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) == ord('q'):
        break
    cap.release()
    cv2.destroyAllWindows()
    print(width, height)

    import cv2
    import numpy as np
    
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
    smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_smile.xml')
    
    cap = cv2.VideoCapture(0)
    width=1280
    height=960
    cap.set(cv2.CAP_PROP_FRAME_WIDTH,width)#设置图像宽度
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT,height)#设置图像高度
    fgbg = cv2.createBackgroundSubtractorMOG2(
    history=500, varThreshold=100, detectShadows=False)#基于自适应混合高斯背景建模的背景减除法，去除干扰
    
    cnt=1
    while(1):
    # get a frame
    ret, frame = cap.read()
    # show a frame
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5,0) #把灰度图片传给haar进行灰度处理，返回值是人脸左上角坐标，宽度和高度
    #1.3为缩放比例，默认为1.1即每次搜索窗口依次扩大10%
    #5为构成检测目标的相邻矩形的最小个数
    #0为flag，表示使用边缘抑制，会使用Canny边缘检测来排除边缘过多或过少的区域
    for(x, y, w, h) in faces:
        
        img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
    
        roi_gray = gray[y:y+h, x:x+w]
    
        eyes = eye_cascade.detectMultiScale(roi_gray, 1.8, 5,0)
    
        roi_color = img[y:y + h, x:x + w]
    
        for(ex, ey, ew, eh) in eyes:
             cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
        smiles = smile_cascade.detectMultiScale(roi_gray, scaleFactor = 1.16,\
                                                minNeighbors= 65, minSize=(25,25), \
                                                flags = cv2.CASCADE_SCALE_IMAGE)
        
        for (ex,ey,ew,eh) in smiles:
            # 画出微笑框，红色（BGR色彩体系），画笔宽度为1
            cv2.rectangle(roi_gray, (ex,ey), (ex+ew,ey+eh), (0,0,255), 1)
            cv2.putText(img, "smile", (x,y-7), 3, 1.2, (0,0,225), 2, cv2.LINE_AA)
            #cv2.LINE_AA 为抗锯齿，这样看起来会非常平滑
            #cv2.imwrite(f"img{cnt}.png",img)
            cnt+=1
    cv2.imshow("camera", frame)
    if cv2.waitKey(5) == ord('q'):
         break
    cap.release()
    cv2.destroyAllWindows()

    #提取红色球球
    import cv2
    import numpy as np
    lowerb_hsv = np.array([156,43,46])
    upperb_hsv = np.array([180,255,255])
    cap = cv2.VideoCapture('redone.mp4')
    while(cap.isOpened()):
    # 获取一帧
    ret, frame = cap.read()
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lowerb_hsv, upperb_hsv)
    redball = cv2.bitwise_and(frame,frame,mask=mask)
    cv2.imshow('frame', redball)    
    if cv2.waitKey(25) == ord('q'):
        break
        
    cap.release()
    cv2.destroyAllWindows()